Table of Contents

Class CodeBERT<T>

Namespace
AiDotNet.ProgramSynthesis.Engines
Assembly
AiDotNet.dll

CodeBERT is a bimodal pre-trained model for programming and natural languages.

public class CodeBERT<T> : CodeModelBase<T>, INeuralNetworkModel<T>, INeuralNetwork<T>, IInterpretableModel<T>, IInputGradientComputable<T>, IDisposable, ICodeModel<T>, IFullModel<T, Tensor<T>, Tensor<T>>, IModel<Tensor<T>, Tensor<T>, ModelMetadata<T>>, IModelSerializer, ICheckpointableModel, IParameterizable<T, Tensor<T>, Tensor<T>>, IFeatureAware, IFeatureImportance<T>, ICloneable<IFullModel<T, Tensor<T>, Tensor<T>>>, IGradientComputable<T, Tensor<T>, Tensor<T>>, IJitCompilable<T>

Type Parameters

T

The numeric type used for calculations (e.g., double, float).

Inheritance
CodeBERT<T>
Implements
IFullModel<T, Tensor<T>, Tensor<T>>
IModel<Tensor<T>, Tensor<T>, ModelMetadata<T>>
IParameterizable<T, Tensor<T>, Tensor<T>>
ICloneable<IFullModel<T, Tensor<T>, Tensor<T>>>
IGradientComputable<T, Tensor<T>, Tensor<T>>
Inherited Members
Extension Methods

Remarks

CodeBERT is designed to understand both code and natural language. It uses a transformer-based encoder architecture pre-trained on code-documentation pairs from GitHub. It excels at tasks like code search, code documentation generation, and code completion.

For Beginners: CodeBERT is an AI that understands programming languages.

Just like BERT understands English, CodeBERT understands code. It's been trained on millions of code examples from GitHub and can:

  • Understand what code does
  • Find similar code
  • Complete code as you write
  • Generate documentation
  • Translate between code and descriptions

Think of it as an AI that's read millions of lines of code and learned the patterns of good programming, just like you learn language by reading books.

Constructors

CodeBERT(CodeSynthesisArchitecture<T>, ILossFunction<T>?, IGradientBasedOptimizer<T, Tensor<T>, Tensor<T>>?, ITokenizer?)

Initializes a new instance of the CodeBERT<T> class.

public CodeBERT(CodeSynthesisArchitecture<T> architecture, ILossFunction<T>? lossFunction = null, IGradientBasedOptimizer<T, Tensor<T>, Tensor<T>>? optimizer = null, ITokenizer? tokenizer = null)

Parameters

architecture CodeSynthesisArchitecture<T>

The architecture configuration for the model.

lossFunction ILossFunction<T>

Optional loss function (defaults to cross-entropy for code tasks).

optimizer IGradientBasedOptimizer<T, Tensor<T>, Tensor<T>>

Optional optimizer (defaults to Adam optimizer).

tokenizer ITokenizer

Optional tokenizer (defaults to a safe built-in tokenizer).

Remarks

Creates a new CodeBERT model with the specified architecture. The model will be initialized with encoder layers suitable for code understanding tasks.

For Beginners: This creates a new CodeBERT model.

You provide:

  • Architecture: The blueprint (size, layers, etc.)
  • Loss function: How to measure mistakes (optional)
  • Optimizer: How to improve from mistakes (optional)
  • Tokenizer: How to convert code into tokens (optional)

Like setting up a new student with a curriculum and teaching method.

Methods

CreateNewInstance()

Creates a new instance of the same type as this neural network.

protected override IFullModel<T, Tensor<T>, Tensor<T>> CreateNewInstance()

Returns

IFullModel<T, Tensor<T>, Tensor<T>>

A new instance of the same neural network type.

Remarks

For Beginners: This creates a blank version of the same type of neural network.

It's used internally by methods like DeepCopy and Clone to create the right type of network before copying the data into it.

DeserializeNetworkSpecificData(BinaryReader)

Deserializes network-specific data that was not covered by the general deserialization process.

protected override void DeserializeNetworkSpecificData(BinaryReader reader)

Parameters

reader BinaryReader

The BinaryReader to read the data from.

Remarks

This method is called at the end of the general deserialization process to allow derived classes to read any additional data specific to their implementation.

For Beginners: Continuing the suitcase analogy, this is like unpacking that special compartment. After the main deserialization method has unpacked the common items (layers, parameters), this method allows each specific type of neural network to unpack its own unique items that were stored during serialization.

GetModelMetadata()

Gets the metadata for this neural network model.

public override ModelMetadata<T> GetModelMetadata()

Returns

ModelMetadata<T>

A ModelMetaData object containing information about the model.

InitializeLayers()

Initializes the layers of the CodeBERT model.

protected override void InitializeLayers()

Remarks

Sets up the encoder layers including embeddings, positional encoding, multi-head attention, and feed-forward networks based on the architecture.

For Beginners: This builds the internal structure of CodeBERT.

Creates all the layers that process code:

  • Embedding layer: Converts code tokens to numbers
  • Attention layers: Let the model focus on important parts
  • Processing layers: Transform and analyze the code

Like assembling the components of a machine according to the blueprint.

SerializeNetworkSpecificData(BinaryWriter)

Serializes network-specific data that is not covered by the general serialization process.

protected override void SerializeNetworkSpecificData(BinaryWriter writer)

Parameters

writer BinaryWriter

The BinaryWriter to write the data to.

Remarks

This method is called at the end of the general serialization process to allow derived classes to write any additional data specific to their implementation.

For Beginners: Think of this as packing a special compartment in your suitcase. While the main serialization method packs the common items (layers, parameters), this method allows each specific type of neural network to pack its own unique items that other networks might not have.

Train(Tensor<T>, Tensor<T>)

Trains the neural network on a single input-output pair.

public override void Train(Tensor<T> input, Tensor<T> expectedOutput)

Parameters

input Tensor<T>

The input data.

expectedOutput Tensor<T>

The expected output for the given input.

Remarks

This method performs one training step on the neural network using the provided input and expected output. It updates the network's parameters to reduce the error between the network's prediction and the expected output.

For Beginners: This is how your neural network learns. You provide: - An input (what the network should process) - The expected output (what the correct answer should be)

The network then:

  1. Makes a prediction based on the input
  2. Compares its prediction to the expected output
  3. Calculates how wrong it was (the loss)
  4. Adjusts its internal values to do better next time

After training, you can get the loss value using the GetLastLoss() method to see how well the network is learning.

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